A digital image is a representation of a two-dimensional image as a set of digital values, called picture elements or pixels. The pixels often are stored in a computer memory as a raster image, a two-dimensional array of small integers. Each pixel of an image is typically associated to a specific “position” in some two-dimensional region, and has a value consisting of one or more quantities (samples) related to that position.
A digital color image includes color information for each pixel. For visually acceptable results, it usually is necessary to provide at least three samples (color channels) for each pixel, which are interpreted as coordinates in some color space. The RGB color space is commonly used in computer displays, but other spaces such as YUV and HSV are often used in other contexts.
Bandwidth is a premium when distributing digital data or content, especially digital color images. Compression algorithm standards such as MPEG1, JPEG, MPEG2, JPEG2K, QuickTime, etc. have been developed and adopted for use by media applications and devices to enable digital audio/visual (AV) distribution. These compression standards achieve bandwidth compression via a variety of different algorithms that are tuned to the human perceptual characteristics and that take advantage of the spatial and temporal redundancy (or correlation) of video content.
The need for higher resolution digital media (High Definition video and beyond) and for more content (e.g., more channels) increases the requirements on bandwidth. This demand is addressed by the use of at least two complementary technology development efforts. The first is the development of sophisticated modulation schemes to increase the total available bandwidth of a given medium (e.g., 802.11x standards, MIMO modes, etc.). The second is the development of new compression algorithms that compress video at a higher rate (e.g,. MPEG4, AVC, VC1, etc.).
The bandwidth requirements for uncompressed digital video can be prohibitive, for example from 300 Mbps for Standard Definition to 2 Gbps for High Definition. Video compression algorithms can greatly reduce bandwidth requirements and often are a mandatory component in many video applications (e.g., broadcast TV over air/cable/satellite, streaming, storage, etc.) that operate in a bandwidth-constrained environment.
Fortunately, natural video is rich in spatial and temporal redundancy or correlation. Most if not all video compression algorithms take advantage of this correlation. The individual coding gain of a typical compression algorithm largely depends on its effectiveness in exploiting the spatial and temporal correlation of video. However, the algorithm needs to perform this while minimizing the loss of perceptual quality of the video. This is due to the fact that compression results in the loss of information that manifests as a degradation of perceptual quality in video. Good compression algorithms balance this trade-off by restricting the information loss to areas that are not easily perceived by the human visual system (e.g., high frequency content) while gaining significant compression.
MPEG1, MPEG2 and MPEG4 are some of the widely-used video compression algorithms in media applications. The emerging Advanced Video Coding (AVC) (MPEG4-part 10) may be the next major video algorithm. Each algorithm, starting with MPEG2, has been a significant improvement from its predecessor in terms of coding gain (compression) and picture quality. For example, the emerging AVC algorithm may have a 50% improved coding gain relative to MPEG2 for about the same picture quality. This is enabled due to AVC's use of variable data block size (e.g., 4×4, 8×8, 4×8, etc.) transforms and the use of enhanced motion estimation and compensation methods as compared with MPEG2. The variable block sizes enable better exploitation of spatial correlation while the enhanced motion estimation and compensation lead to more effective extraction of temporal correlation. In summary, video compression algorithms rely on advanced image processing techniques to obtain higher compression gains.
Yet despite these advancements in data compression, there remains a need for yet further improvements in this field.